Method on moving obstacle representation for trajectory planning

ABSTRACT

According to some embodiments, a system operates an ADV. In one embodiment, the system perceives a driving environment surrounding the ADV based on sensor data obtained from a plurality of sensors, including perceiving a moving obstacle that is moving relative to the ADV. The system projects the moving obstacle as a figure onto a station-time (ST) coordinate system, wherein the ST coordinate system indicates a distance between the figure and a reference point at different points in time. And the system, for each of a plurality of predetermined processing time intervals, determines two points of the figure in the ST coordinate system to represent a shape of the figure, wherein the shape of the figure is utilized to plan a trajectory to drive the ADV to avoid colliding with the moving obstacle.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to ST boundaries for autonomous driving vehicles (ADVs).

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

Vehicles can navigate using reference lines. A reference line is a paththat autonomous driving vehicles should drive along in an idealcondition when there are no surrounding obstacles. Station-time (ST)boundary can be used by an ADV to model surrounding obstacles.

For autonomous driving, a blocking obstacle's ST boundary can be a setof points (s, t) that reflects at time t, an obstacle will block theADV's path at position s (e.g., blocking obstacles). These ST boundariesare mapped onto a ST Graph for the ADV to avoid hitting the STboundaries. Moreover, obstacles that may influence a driver's behavior(e.g., neighboring vehicle's blind zone) but do not block an ADV's pathmay be modeled with non-blocking ST boundaries. Examples of theseobstacles include a decision not to bypass a vehicle of an adjacentlane, a neighboring vehicle's blind zone, staying out of a keep clearzone, or a decision to yield to relatively large vehicles.

Trajectory planning is an important component in the Autonomous Drivingtechnology. A moving obstacle may be modeled as a closed figure in theST coordinate system (e.g., on an ST graph). Traditionally, aquadrilateral is used to represent the closed figure (in other words,four points on the boundary of the figure are used to represent themoving obstacle in the ST coordinate system) in the actual operation ofthe ADV, which frequently introduces inaccuracies due to inconsistenciesbetween the actual shape of the figure associated with the obstacle onthe ST graph and the quadrilateral representation of the figure. Theinaccuracies can cause planning failure and/or other safety issues.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomousvehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating examples of a perception andplanning system used by an autonomous vehicle according to someembodiments.

FIG. 4 is a block diagram illustrating an example of a decision and aplanning processes according to one embodiment.

FIG. 5 is a block diagram illustrating an example of a decision moduleaccording to one embodiment.

FIG. 6 is a diagram illustrating a conventional method for representinga moving obstacle that is modeled with a figure on the ST graph.

FIG. 7 is a diagram illustrating a method for representing a movingobstacle on the ST graph according to one embodiment of the disclosure.

FIG. 8 is a flow diagram illustrating a method according to oneembodiment.

FIG. 9 is a block diagram illustrating a data processing systemaccording to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to some embodiments, a system operates an ADV. In oneembodiment, the system perceives a driving environment surrounding theADV based on sensor data obtained from a plurality of sensors, includingperceiving a moving obstacle that is moving relative to the ADV. Thesystem projects the moving obstacle as a figure onto a station-time (ST)coordinate system, wherein the ST coordinate system indicates a distancebetween the figure and a reference point at different points in time.And the system, for each of a plurality of predetermined processing timeintervals, determines two points of the figure in the ST coordinatesystem to represent a shape of the figure, wherein the shape of thefigure is utilized to plan a trajectory to drive the ADV to avoidcolliding with the moving obstacle.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) severs, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured into anautonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113, and sensorsystem 115. Autonomous vehicle 101 may further include certain commoncomponents included in ordinary vehicles, such as, an engine, wheels,steering wheel, transmission, etc., which may be controlled by vehiclecontrol system 111 and/or perception and planning system 110 using avariety of communication signals and/or commands, such as, for example,acceleration signals or commands, deceleration signals or commands,steering signals or commands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn control the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using Wi-Fi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyword, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or predictive models 124 for avariety of purposes. In one embodiment, for example, algorithms 124 mayinclude an optimization method to optimize path planning and speedplanning. The optimization method may include a set of cost functionsand polynomial functions to represent path segments or time segments.These functions can be uploaded onto the autonomous driving vehicle tobe used to generate a smooth path at real time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, and routing module 307.

Some or all of modules 301-307 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-307may be integrated together as an integrated module. For example,decision module 304 and planning module 305 may be integrated as asingle module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration (e.g., straight or curvelanes), traffic light signals, a relative position of another vehicle, apedestrian, a building, crosswalk, or other traffic related signs (e.g.,stop signs, yield signs), etc., for example, in a form of an object.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle). That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, and turning commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as command cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or command cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to effect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

Decision module 304/planning module 305 may further include a collisionavoidance system or functionalities of a collision avoidance system toidentify, evaluate, and avoid or otherwise negotiate potential obstaclesin the environment of the autonomous vehicle. For example, the collisionavoidance system may effect changes in the navigation of the autonomousvehicle by operating one or more subsystems in control system 111 toundertake swerving maneuvers, turning maneuvers, braking maneuvers, etc.The collision avoidance system may automatically determine feasibleobstacle avoidance maneuvers on the basis of surrounding trafficpatterns, road conditions, etc. The collision avoidance system may beconfigured such that a swerving maneuver is not undertaken when othersensor systems detect vehicles, construction barriers, etc. in theregion adjacent the autonomous vehicle that would be swerved into. Thecollision avoidance system may automatically select the maneuver that isboth available and maximizes safety of occupants of the autonomousvehicle. The collision avoidance system may select an avoidance maneuverpredicted to cause the least amount of acceleration in a passenger cabinof the autonomous vehicle.

Routing module 307 can generate reference routes, for example, from mapinformation such as information of road segments, vehicular lanes ofroad segments, and distances from lanes to curb. For example, a road canbe divided into sections or segments {A, B, and C} to denote three roadsegments. Three lanes of road segment A can be enumerated {A1, A2, andA3}. A reference route is generated by generating reference points alongthe reference route. For example, for a vehicular lane, routing module307 can connect midpoints of two opposing curbs or extremities of thevehicular lane provided by a map data. Based on the midpoints andmachine learning data representing collected data points of vehiclespreviously driven on the vehicular lane at different points in time,routing module 307 can calculate the reference points by selecting asubset of the collected data points within a predetermined proximity ofthe vehicular lane and applying a smoothing function to the midpoints inview of the subset of collected data points.

Based on reference points or lane reference points, routing module 307may generate a reference line by interpolating the reference points suchthat the generated reference line is used as a reference line forcontrolling ADVs on the vehicular lane. In some embodiments, a referencepoints table and a road segments table representing the reference linesare downloaded in real-time to ADVs such that the ADVs can generatereference lines based on the ADVs' geographical location and drivingdirection. For example, in one embodiment, an ADV can generate areference line by requesting routing service for a path segment by apath segment identifier representing an upcoming road section aheadand/or based on the ADV's GPS location. Based on a path segmentidentifier, a routing service can return to the ADV reference pointstable containing reference points for all lanes of road segments ofinterest. ADV can look up reference points for a lane for a path segmentto generate a reference line for controlling the ADV on the vehicularlane.

As described above, route or routing module 307 manages any data relatedto a trip or route of a user. The user of the ADV specifies a startingand a destination location to obtain trip related data. Trip relateddata includes route segments and a reference line or reference points ofthe route segment. For example, based on route map info 311, routemodule 307 generates a route or road segments table and a referencepoints table. The reference points are in relations to road segmentsand/or lanes in the road segments table. The reference points can beinterpolated to form one or more reference lines to control the ADV. Thereference points can be specific to road segments and/or specific lanesof road segments.

For example, a road segments table can be a name-value pair to includeprevious and next road lanes for road segments A-D. E.g., a roadsegments table may be: {(A1, B1), (B1, C1), (C1, D1)} for road segmentsA-D having lane 1. A reference points table may include reference pointsin x-y coordinates for road segments lanes, e.g., {(A1, (x1, y1)), (B1,(x2, y2)), (C1, (x3, y3)), (D1, (x4, y4))}, where A1 . . . D1 refers tolane 1 of road segments A-D, and (x1, y1) . . . (x4, y4) arecorresponding real world coordinates. In one embodiment, road segmentsand/or lanes are divided into a predetermined length such asapproximately 200 meters segments/lanes. In another embodiment, roadsegments and/or lanes are divided into variable length segments/lanesdepending on road conditions such as road curvatures. In someembodiments, each road segment and/or lane can include several referencepoints. In some embodiments, reference points can be converted to othercoordinate systems, e.g., latitude-longitude.

In some embodiments, reference points can be converted into a relativecoordinates system, such as station-lateral (SL) coordinates. Astation-lateral coordinate system is a coordinate system that referencesa fixed reference point to follow a reference line. For example, a (S,L)=(1, 0) coordinate can denote one meter ahead of a stationary point(i.e., the reference point) on the reference line with zero meterlateral offset. A (S, L)=(2, 1) reference point can denote two metersahead of the stationary reference point along the reference line and anone meter lateral offset from the reference line, e.g., offset to theleft by one meter.

In one embodiment, decision module 304 generates a rough path profilebased on a reference line provided by routing module 307 and based onobstacles and/or traffic information perceived by the ADV, surroundingthe ADV. The rough path profile can be a part of path/speed profiles 313which may be stored in persistent storage device 352. The rough pathprofile is generated by selecting points along the reference line. Foreach of the points, decision module 304 moves the point to the left orright (e.g., candidate movements) of the reference line based on one ormore obstacle decisions on how to encounter the object, while the restof points remain steady. The candidate movements are performediteratively using dynamic programming to path candidates in search of apath candidate with a lowest path cost using cost functions, as part ofcosts functions 315 of FIG. 3A, thereby generating a rough path profile.Examples of cost functions include costs based on: a curvature of aroute path, a distance from the ADV to perceived obstacles, and adistance of the ADV to the reference line. In one embodiment, thegenerated rough path profile includes a station-lateral map, as part ofSL maps/ST graphs 314 which may be stored in persistent storage devices352.

In one embodiment, decision module 304 generates a rough speed profile(as part of path/speed profiles 313) based on the generated rough pathprofile. The rough speed profile indicates the best speed at aparticular point in time controlling the ADV. Similar to the rough pathprofile, candidate speeds at different points in time are iterated usingdynamic programming to find speed candidates (e.g., speed up or slowdown) with a lowest speed cost based on cost functions, as part of costsfunctions 315 of FIG. 3A, in view of obstacles perceived by the ADV. Therough speed profile decides whether the ADV should overtake or avoid anobstacle, and to the left or right of the obstacle. In one embodiment,the rough speed profile includes a station-time (ST) graph (as part ofSL maps/ST graphs 314). Station-time graph indicates a distancetravelled with respect to time.

In one embodiment, the rough path profile is recalculated by optimizinga path cost function (as part of cost functions 315) using quadraticprogramming (QP). In one embodiment, the recalculated rough path profileincludes a station-lateral map (as part of SL maps/ST graphs 314). Inone embodiment, planning module 305 recalculates the rough speed profileusing quadratic programming (QP) to optimize a speed cost function (aspart of cost functions 315). In one embodiment, the recalculated roughspeed profile includes a station-time graph (as part of SL maps/STgraphs 314).

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

FIG. 4 is a block diagram illustrating an example of a decision andplanning process according to one embodiment. FIG. 5 is a block diagramillustrating an example of a decision module according to oneembodiment. Referring to FIG. 4, Decision and planning process 400includes localization/perception data 401, path decision process 403,speed decision process 405, path planning process 407, speed planningprocess 409, aggregator 411, and trajectory calculator 413.

Path decision process 403 and speed decision process 405 may beperformed respectively by a path decision module 501 and a speeddecision module 503 of decision module 304 in FIG. 5. Referring to FIGS.4-5, path decision process 403 or path decision module 501 includes pathstate machine 505, path traffic rules 507, and station-lateral mapsgenerator 509. In one embodiment, path decision process 403 or pathdecision module 501 can generate a rough path profile, using dynamicprogramming (via DP module 520), as an initial condition for speeddecision process 405. For example, path state machine 505 can include atleast three states: cruising, changing lane, and idle states. Path statemachine 505 provides previous planning results and important informationsuch as whether the ADV is cruising or changing lanes. Path trafficrules 507, as part of driving/traffic rules 312 of FIG. 3A, includetraffic rules that can affect the outcome of a path decisions module.For example, path traffic rules 507 can include traffic information suchas construction traffic signs such that the ADV can avoid road lanesunder construction. From the path states, traffic rules, a referenceline provided by routing module 307, and obstacles perceived by thesensor systems of the ADV, path decision process 403 can decide how theperceived obstacles are handled (i.e., ignore, yield, stop, or pass), aspart of a rough path profile.

For example, in one embedment, the rough path profile is generated by acost function consisting of costs based on: a curvature of path and adistance from the reference line and/or reference points to theperceived obstacles. Points on the reference line are selected and aremoved to the left or right of the reference lines as candidate movementsrepresenting path candidates. Each of the candidate movements has anassociated cost. The associated costs for candidate movements of one ormore points on the reference line can be solved using dynamicprogramming for an optimal cost sequentially, one point at a time. Inone embodiment, SL maps generator 509 generates a station-lateral (SL)map as part of the rough path profile. Here, the perceived obstacles canbe modeled as SL boundaries of the SL map. A SL map is a two-dimensionalgeometric map (similar to an x-y coordinate plane) that includesobstacles information (or SL boundaries) perceived by the ADV. Thegenerated SL map lays out an ADV path for controlling the ADV. Note,dynamic programming (or dynamic optimization) is a mathematicaloptimization method that breaks down a problem to be solved into asequence of value functions, solving each of these value functions justonce and storing their solutions. The next time the same value functionoccurs, the previous computed solution is simply looked up savingcomputation time instead of recomputing its solution.

As described above, obstacles perceived by the ADV for the path decisionprocess 403 include obstacles as SL boundaries. Speed decision process405, however, includes obstacles as ST boundaries (e.g., boundaries of astation-time (ST) graph). In one embodiment, ST boundaries includeblocking and non-blocking boundaries. Note, blocking boundariescorresponds to obstacles that block a path of the ADV. Non-blockingboundaries correspond to obstacles that do not block a path of the ADVbut can be modeled to influence a speed of the ADV.

Speed decision process 405 or speed decision module 503 includes speedstate machine 511, speed traffic rules 513, station-time graphsgenerator 515, and obstacles determiner 517. Speed decision process 405or speed decision module 503 can generate a rough speed profile usingdynamic optimization or dynamic programming (via dynamic programming(DP) module 520) as an initial condition for path/speed planningprocesses 407 and 409. In one embodiment, speed state machine 511includes at least two states: speed up and slow down states. Speedtraffic rules 513, as part of driving/traffic rules 312 of FIG. 3A,include traffic rules that can affect the outcome of a speed decisionsmodule. For example, speed traffic rules 513 can include trafficinformation such as red/green traffic lights, another vehicle in acrossing route, etc. From a state of the speed state machine, speedtraffic rules and/or perceived obstacles, and rough path profile/SL mapgenerated by decision process 403, speed decision process 405 cangenerate a rough speed profile to control a speed of the ADV in view ofblocking and non-blocking obstacles. Non-blocking obstacles can bedetermined by obstacles determiner 517. ST graphs generator 515 can thengenerate a ST graph as part of the rough speed profile.

Referring to FIG. 4, path planning process 407 can use a rough pathprofile (e.g., a SL map from path decision process 403) as the initialcondition to recalculate an optimal SL curve or S path using quadraticprogramming. Quadratic programming involves minimizing or maximizing anobjective function (e.g., a quadratic function with several variables)subject to bounds, linear equality, and/or inequality constraints.

Speed planning process 409 can use a rough speed profile (e.g., a STgraph from speed decision process 405) as initial condition torecalculate an optimal ST curve using quadratic programming.

Aggregator 411 performs the function of aggregating the path and speedplanning results. For example, in one embodiment, aggregator 411 cancombine the ST graph and the SL map into a SLT graph. In anotherembodiment, aggregator 411 can interpolate (or fill in additionalpoints) based on 2 consecutive points on a SL reference line or STcurve. In another embodiment, aggregator 411 can translate referencepoints from (S, L) coordinates to (x, y) coordinates. Trajectorygenerator 413 can calculate the final trajectory to control the ADV. Forexample, based on the SLT graph provided by aggregator 411, trajectorygenerator 413 calculates a list of (x, y, T) points indicating at whattime should the ADV pass a particular (x, y) coordinate.

Thus, referring back to FIG. 4, path decision process 403 and speeddecision process 405 are to generate a rough path profile and a roughspeed profile taking into consideration obstacles (blocking andnon-blocking) and/or traffic conditions. Given all the path and speeddecisions regarding the obstacles, path planning process 407 and speedplanning process 409 are to optimize the rough path profile and thespeed profile in view of the obstacles using QP programming to generatean optimal trajectory with minimum path cost and/or speed cost.

Non-blocking obstacles may be determined by projecting predeterminedregions outwardly from a left and a right side of the rough trajectoryand calculating if obstacles perceived by the ADV will overlap thesepredetermined regions. Generating a speed profile may include performinga dynamic optimization on a speed of the ADV based on the path profileand in view of the non-blocking obstacles. Performing a dynamicoptimization may include optimizing a cost function to generate astation-time graph for the speed profile.

Non-blocking obstacles may correspond to one or more of: 1) a decisionnot to bypass a neighboring vehicle, 2) a decision to keep clear from ablind zone region of a neighboring vehicle, or 3) a decision to keepclear from a particular road region.

Referring to FIG. 6, a diagram 600 illustrating a conventional methodfor representing a moving obstacle that is modeled with a figure on theST graph is shown. In the ST coordinate system, a quadrilateral 620 isused to represent the figure 610, which models a moving obstacle. Inother words, four points on the boundary of the figure 610 are used torepresent the obstacle with which the figure 610 is associated in the STcoordinate system when relevant operations of the ADV are performed.Inaccuracies due to inconsistencies between the actual shape of thefigure 610 and the quadrilateral representation 620 of the figure 610are introduced as a result. The inaccuracies may cause planning failureand/or other safety issues.

A naïve solution to the problem associated with the representationinaccuracy described above is to increase the number of points on theboundary of the figure 610 used in the operations. In other words, apolygon with more than four sides may be used to represent the obstacleto increase accuracy. However, increasing the number of points maysignificantly increase the processing time needed because the timecomplexity associated with increasing the number of points is O(N),where N is the number of points.

Referring to FIG. 7, a diagram 700 illustrating a method forrepresenting a moving obstacle on the ST graph according to oneembodiment of the disclosure is shown. At each of a plurality ofprocessing occasions, two points in the ST coordinate system, one upperand one lower, are determined to represent the moving obstacle. Thus,the two points are located on the boundary of the figure that models theobstacle in the ST coordinate system and correspond to the same time.The processing occasions may be spaced by a uniform time interval (e.g.,0.1, 0.2, or 0.3 seconds, etc.). In other words, after every processingtime interval, the pair of points on the ST graph are determined.Adjacent pairs of points may be merged. For example, in FIG. 7, at aprocessing occasion 701, an upper point 711A and a lower point 711B aredetermined in the ST coordinate system to represent the moving obstacle.The pair of points 711A, 711B are located on the boundary of the figure720 that models the obstacle. Similarly, at processing occasions702-704, respective pairs of points (712A, 712B)-(714A, 714B) in the STcoordinate system are determined to represent the moving obstacle. Theprocessing occasions 701-704 may be spaced by a uniform processing timeinterval (e.g., 0.1, 0.2, or 0.3 seconds, etc.). It should beappreciated that representing a moving obstacle in the ST coordinatesystem using the method illustrated in FIG. 7 is associated with a timecomplexity of O(lgN), where N is the number of points (upper and lower),which is a significant improvement over the O(N) time complexity. Thus,quick operations (search, find, evaluate, etc.) may be achieved.

FIG. 8 is a flow diagram illustrating a method for operating an ADV.Processing 800 may be performed by processing logic which may includesoftware, hardware, or a combination thereof. For example, process 800may be performed by speed decision module 503 of FIG. 5. Referring toFIG. 8, at block 801, a driving environment surrounding the ADV may beperceived based on sensor data obtained from a plurality of sensors,including perceiving a moving obstacle that is moving relative to theADV. At block 802, the moving obstacle may be projected as a figure ontoa station-time (ST) coordinate system, wherein the ST coordinate systemindicates a distance between the figure and a reference point atdifferent points in time. At block 803, for each of a plurality ofpredetermined processing time intervals, two points of the figure may bedetermined in the ST coordinate system to represent a shape of thefigure, wherein the shape of the figure is utilized to plan a trajectoryto drive the ADV to avoid colliding with the moving obstacle.

Further, the trajectory for the ADV may be planned based on the pointsof the figure representing the moving obstacle in the ST coordinatesystem, including performing a speed optimization of the trajectory. Thetwo points determined at each processing time interval may comprise oneupper point and one lower point that intercept a perimeter of thefigure, wherein the upper point and the lower point correspond to anidentical time. Adjacent pairs of points representing the movingobstacle in the ST coordinate system may be merged.

FIG. 9 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the disclosure. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, perception and planning system 110 or anyof servers 103-104 of FIG. 1. System 1500 can include many differentcomponents. These components can be implemented as integrated circuits(ICs), portions thereof, discrete electronic devices, or other modulesadapted to a circuit board such as a motherboard or add-in card of thecomputer system, or as components otherwise incorporated within achassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 connected via a bus or an interconnect 1510. Processor1501 may represent a single processor or multiple processors with asingle processor core or multiple processor cores included therein.Processor 1501 may represent one or more general-purpose processors suchas a microprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows® operating system from Microsoft®, Mac OS®/iOS® from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include IO devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional IO device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other IO devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions or software(e.g., module, unit, and/or logic 1528) embodying any one or more of themethodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, path decision module 501, and speeddecision module 503 of FIG. 5. Processing module/unit/logic 1528 mayalso reside, completely or at least partially, within memory 1503 and/orwithin processor 1501 during execution thereof by data processing system1500, memory 1503 and processor 1501 also constitutingmachine-accessible storage media. Processing module/unit/logic 1528 mayfurther be transmitted or received over a network via network interfacedevice 1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present disclosure. Itwill also be appreciated that network computers, handheld computers,mobile phones, servers, and/or other data processing systems which havefewer components or perhaps more components may also be used withembodiments of the disclosure.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method for operating anautonomous driving vehicle (ADV), the method comprising: perceiving adriving environment surrounding the ADV based on sensor data obtainedfrom a plurality of sensors, including perceiving a moving obstacle thatis moving relative to the ADV; projecting the moving obstacle as afigure onto a station-time (ST) coordinate system, wherein the STcoordinate system indicates a distance between the figure and areference point at different points in time; and for each of a pluralityof predetermined processing time intervals, determining two points ofthe figure in the ST coordinate system to represent a shape of thefigure, wherein the shape of the figure is utilized to plan a trajectoryto drive the ADV to avoid colliding with the moving obstacle.
 2. Thecomputer-implemented method of claim 1, further comprising planning thetrajectory for the ADV based on the points of the figure representingthe moving obstacle in the ST coordinate system, including performing aspeed optimization of the trajectory.
 3. The computer-implemented methodof claim 1, wherein the two points determined at each processing timeinterval comprise one upper point and one lower point that intercept aperimeter of the figure.
 4. The computer-implemented method of claim 3,wherein the upper point and the lower point correspond to an identicaltime.
 5. The computer-implemented method of claim 1, wherein theprocessing time interval is a uniform time interval.
 6. Thecomputer-implemented method of claim 5, wherein the processing timeinterval is approximately 0.2 seconds.
 7. The computer-implementedmethod of claim 1, further comprising merging adjacent pairs of pointsrepresenting the moving obstacle in the ST coordinate system.
 8. Anon-transitory machine-readable medium having instructions storedtherein, which when executed by a processor, cause the processor toperform operations for operating an autonomous driving vehicle (ADV),the operations comprising: perceiving a driving environment surroundingthe ADV based on sensor data obtained from a plurality of sensors,including perceiving a moving obstacle that is moving relative to theADV; projecting the moving obstacle as a figure onto a station-time (ST)coordinate system, wherein the ST coordinate system indicates a distancebetween the figure and a reference point at different points in time;and for each of a plurality of predetermined processing time intervals,determining two points of the figure in the ST coordinate system torepresent a shape of the figure, wherein the shape of the figure isutilized to plan a trajectory to drive the ADV to avoid colliding withthe moving obstacle.
 9. The non-transitory machine-readable medium ofclaim 8, further comprising planning the trajectory for the ADV based onthe points of the figure representing the moving obstacle in the STcoordinate system, including performing a speed optimization of thetrajectory.
 10. The non-transitory machine-readable medium of claim 8,wherein the two points determined at each processing time intervalcomprise one upper point and one lower point that intercept a perimeterof the figure.
 11. The non-transitory machine-readable medium of claim10, wherein the upper point and the lower point correspond to anidentical time.
 12. The non-transitory machine-readable medium of claim8, wherein the processing time interval is a uniform time interval. 13.The non-transitory machine-readable medium of claim 12, wherein theprocessing time interval is approximately 0.2 seconds.
 14. Thenon-transitory machine-readable medium of claim 8, further comprisingmerging adjacent pairs of points representing the moving obstacle in theST coordinate system.
 15. A data processing system, comprising: aprocessor; and a memory coupled to the processor to store instructions,which when executed by the processor, cause the processor to performoperations for operating an autonomous driving vehicle (ADV), theoperations including perceiving a driving environment surrounding theADV based on sensor data obtained from a plurality of sensors, includingperceiving a moving obstacle that is moving relative to the ADV;projecting the moving obstacle as a figure onto a station-time (ST)coordinate system, wherein the ST coordinate system indicates a distancebetween the figure and a reference point at different points in time;and for each of a plurality of predetermined processing time intervals,determining two points of the figure in the ST coordinate system torepresent a shape of the figure, wherein the shape of the figure isutilized to plan a trajectory to drive the ADV to avoid colliding withthe moving obstacle.
 16. The data processing system of claim 15, theoperations further comprising planning the trajectory for the ADV basedon the points of the figure representing the moving obstacle in the STcoordinate system, including performing a speed optimization of thetrajectory.
 17. The data processing system of claim 15, wherein the twopoints determined at each processing time interval comprise one upperpoint and one lower point that intercept a perimeter of the figure. 18.The data processing system of claim 17, wherein the upper point and thelower point correspond to an identical time.
 19. The data processingsystem of claim 15, wherein the processing time interval is a uniformtime interval.
 20. The data processing system of claim 19, wherein theprocessing time interval is approximately 0.2 seconds.
 21. The dataprocessing system of claim 15, the operations further comprising mergingadjacent pairs of points representing the moving obstacle in the STcoordinate system.